One or more aspects relate, in general, to monitoring processing environments, and in particular, to identifying anomalies in such environments.
Large-scale hosting infrastructures and platforms form many processing environments including those having computing systems used in cloud computing and enterprise data centers, as examples. The size of these computing systems, the number of transactions that are performed by the systems, and the large amount of data processed render these systems vulnerable to anomalies. An anomaly is an unexpected change in incoming data or a pattern in the incoming data that deviates from the expected behavior. Anomalies arise from, for example, bottlenecks within the system, memory leaks, hardware failures, etc.
Monitoring data on complex computing systems for anomalies and recognizing anomalies in real-time prevent such anomalies from accumulating and effecting the efficiency of the system and, in a worse case scenario, causing the system, or a portion of the system, to fail. Monitoring such systems and detecting anomalous behavior from collected data streams, however, is not a trivial task. The large number of servers or processors, the traffic, the density of computational clusters and the complex interaction among system components pose serious monitoring and management challenges.